US5436850A - Method to identify protein sequences that fold into a known three-dimensional structure - Google Patents

Method to identify protein sequences that fold into a known three-dimensional structure Download PDF

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US5436850A
US5436850A US08/218,685 US21868594A US5436850A US 5436850 A US5436850 A US 5436850A US 21868594 A US21868594 A US 21868594A US 5436850 A US5436850 A US 5436850A
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David Eisenberg
James U. Bowie
Roland Luthy
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    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K1/00—General methods for the preparation of peptides, i.e. processes for the organic chemical preparation of peptides or proteins of any length
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G16B15/20—Protein or domain folding

Definitions

  • This invention relates to a computer-assisted method for identifying protein sequences that fold into a known three-dimensional structure.
  • Proteins are linear polymers of amino acids. The polymerization reaction which produces a protein results in the loss of one molecule of water from each amino acid, and hence proteins are often said to be composed of amino acid "residues.” Natural protein molecules may contain as many as 20 different types of amino acid residues, each of which contains a distinctive side chain. The particular linear sequence of amino acid residues in a protein defines the primary sequence, or primary structure, of the protein. The primary structure of a protein can be determined with relative ease using known methods.
  • Proteins fold into a three-dimensional structure. The folding is determined by the sequence of amino acids and by the protein's environment. Examination of the three-dimensional structure of numerous natural proteins has revealed a number of recurring patterns. Patterns known as alpha helices, parallel beta sheets, and anti-parallel beta sheets are the most common observed. A description of such protein patterns is provided by Dickerson, R. E., et al. in The Structure and Action of Proteins, W. A. Benjamin, Inc. California (1969). The assignment of each amino acid residue to one of these patterns defines the secondary structure of the protein.
  • the helices, sheets, and turns of a protein's secondary structure pack together to produce the folded three-dimensional, or tertiary, structure of the protein.
  • the three-dimensional structure of many proteins may be characterized as having internal surfaces (directed away from the aqueous environment in which the protein is normally found) and external surfaces (which are exposed to the aqueous environment).
  • hydrophobic residues such as tryptophan, phenylalanine, tyrosine, leucine, isoleucine, valine, or methionine
  • hydrophilic residues such as aspartate, asparagine, glutamate, glutamine, lysine, arginine, histidine, serine, threonine, glycine, and proline
  • the amino acids alanine, glycine, serine, and threonine are encountered with more nearly equal frequency on both the internal and external protein surfaces.
  • the biological properties of proteins depend directly on the proteins three-dimensional (3D) conformation.
  • the 3D conformation determines the activity of enzymes, the capacity and specificity of binding proteins, and the structural attributes of receptor molecules. Because the three-dimensional structure of a protein molecule is so significant, it has long been recognized that a means for readily determining a protein's three-dimensional structure from its known amino acid sequence would be highly desirable. However, it has proved extremely difficult to make such a determination.
  • One difficulty is that each protein has an astronomical number of possible conformations (about 10 16 for a small protein of 100 residues; see K. A. Dill, Biochemistry, 24, 1501-1509, 1985), and there is no reliable method for picking the one conformation stable in aqueous solution.
  • a second difficulty is that there are no accurate and reliable force laws for the interaction of one part of a protein with another part, and with water. Proteins exist in a dynamic equilibrium between a folded, ordered state and an unfolded, disordered state. These and other factors have contributed to the enormous complexity of determining the most probable relative 3D location of each residue in a known protein sequence.
  • the inverse protein folding problem is most often approached by seeking sequences that are similar to the sequence of a protein whose structure is known. If a sequence relationship can be found, it can often be inferred that the protein of known sequence but unknown structure adopts a fold similar to the protein of known structure.
  • the strategy works well for closely related sequences, but structural similarities can go undetected as the level of sequence identity drops below about 25 percent.
  • the rules used to relate one-dimensional amino acid sequences to possible three-dimensional structures in the tertiary template method may be excessively rigid. Proteins that fold into similar structures can have large differences in the size and shape of residues at equivalent positions. These changes are tolerated not only because of replacements or movements in nearby side-chains, but also as a result of shifts in the protein backbone. Moreover, insertions and deletions in the amino acid sequence, which are commonly found in related protein structures, are not considered in the implementation of tertiary templates. To describe realistically the sequence requirements of a particular fold, the constraints of a rigid backbone and a fixed spacing between core residues must somehow be relaxed.
  • Another approach is a profile method that characterizes the amino acid sequences of families of proteins aligned by sequence or structural similarities.
  • the profile method builds a table of weighted values that reflect the frequency that amino acid residues are likely to be located at a particular position in the sequence of amino acids forming the proteins.
  • the profile table thus characterizes the entire family of proteins upon which the table is based.
  • a target amino acid sequence is compared to the profile, using a known dynamic programming method, to determine a final "best fit" score. Insertions and deletions of amino acids in the target sequence are provided for by appropriate "gap opening” and "gap extension” penalties that affect the final score. See M. Gribskov, A. D. McLachlan, and D.
  • Eisenberg Proc. Natl. Acad. Sci. U.S.A., 84, 4355 (1987); M. Gribskov, M. Homyak, J. Edenfield, and D. Eisenberg, CABIOS 4, (1988); M. Gribskov and D. Eisenberg, in "Techniques in Protein Chemistry” (T. E. Hugli, ed.), p. 108. Academic Press, San Diego, Calif., 1989; M. Gribskov, R. Luthy, and D. Eisenberg, Meth. in Enz. 183, 146 (1990).
  • the profile method is useful for learning whether a target protein sequence belongs to a known family of sequences, and some inferences can be made that the target sequence has a three-dimensional structure similar to the structures of the known family of sequences.
  • the profile method does not directly take into account specific structural characteristics of the known family of sequences, since the profile table is constructed based only upon alignments of amino acid sequences within selected proteins of known structure. Thus, a large amount of information inherent in a known structure is simply ignored in a sequence profile.
  • the present invention provides such a method, using a novel method of profiling structural characteristics of families of proteins with known three-dimensional structures, and a computer-assisted search procedure for comparing target amino acid sequences to such profiles.
  • the present invention establishes a link between known three-dimensional structures and target amino acid sequences in a way that simulates the malleability of real proteins.
  • the inventive method attacks the inverse protein folding problem by finding target sequences that are most compatible with profiles representing the structural environments of the residues in known three-dimensional protein structures.
  • the method starts with a known three-dimensional protein structure and determines three key features of each residue's environment within the structure: (1) the total area of the residue's side-chain that is buried by other protein atoms, inaccessible to solvent; (2) the fraction of the side-chain area that is covered by polar atoms (O, N) or water, and (3) the local secondary structure. Based on these parameters, each residue position is categorized into an "environment class". In this manner, a three-dimensional protein structure is converted into a one-dimensional "environment string", or characterizing sequence, which represents the environment class of each residue in the folded protein structure. A 3D structure profile table is created containing score values that represent the frequency of finding any of the 20 common amino acids structures at each position of the environment string.
  • the method determines the most favorable alignment of a target protein sequence to the residue positions defined by the environment string, and determines a "best fit" alignment score, S ij , for the target sequence.
  • S ij a "best fit" alignment score
  • Each target sequence may then be further characterized by a ZScore, which is the number of standard deviations that S ij for the target sequence is above tile mean alignment score for other target sequences of similar length.
  • Examples of the method are presented for four families of proteins--the globins, cyclic AMP receptor-like proteins, the periplasmic binding proteins, and the actins.
  • the method indicates that several repressors have a folding domain that is similar to that of the periplasmic binding proteins.
  • the method is able to detect the structural similarity of the actins and 70K heat shock proteins, even though these proteins share no detectable sequence similarity.
  • FIG. 1 is a diagram of the preferred method for determining an environment string, defining a 3D structure profile table, and performing a 3D compatibility search in accordance with the present invention.
  • FIG. 2 is a diagram showing six of the preferred side-chain environmental classes used to determine an environment string in accordance with the present invention.
  • FIG. 3 is part of the 3D structure profile table for sperm whale myoglobin, in accordance with the present invention.
  • FIG. 4 is a graph showing the results of a 3D compatibility search for the structure of sperm whale myoglobin
  • FIG. 5a is a graph showing the results of a prior art sequence homology search using a sequence profile constructed from the E. Coli ribose binding protein.
  • FIG. 5b is a graph showing the results of a structure compatibility search using a 3D structure profile constructed from the E. coli ribose binding protein structure, using the present invention.
  • FIG. 6 is Table I, representing environment classes for matching 20 common amino acids.
  • FIG. 7 is Table II, representing proteins with Zscores greater than 6.0, derived from a sequence homology search using a sequence profile constructed from the cyclic AMP receptor protein (CRP) sequence with a 3D compatibility search using a 3D structure profile of the CRP structure.
  • CRP cyclic AMP receptor protein
  • FIG. 8 is Table III, representing top scoring proteins derived from a 3D structure profile and a 3D compatibility search.
  • FIG. 1 An overview of the inventive method is diagrammatically shown in FIG. 1.
  • the method starts with a known three-dimensional protein structure P and determines three key features of each residue's environment within the structure:
  • each residue position is categorized into an "environment class".
  • a three-dimensional protein structure P is converted into a one-dimensional "environment string", or characterizing sequence, E which represents the environment class of each residue in the folded protein structure.
  • a 3D structure profile table T is then created containing score values that represent the frequency of finding any of the 20 common amino acids structures at each position of the environment string E. These frequencies are determined from a database of known protein structures and aligned sequences. Thereafter, using known search techniques, the method determines the most favorable alignment of a target protein sequence S to the residue positions defined by the environment string E, and determines a "best fit" score S ij for the target sequence S with respect to the 3D structure profile table T. Each target sequence may then be further characterized by a ZScore, which is the number of standard deviations that S ij for the target sequence is above the mean alignment score for other target sequences of similar length.
  • the alignment of an environment string with a protein sequence relies on the clear preferences of each of the twenty amino acids for different environmental classes. For example, it is rare to find a charged residue buried in a non-polar environment. Thus, by determining the environment class of a given position in a protein structure, it is possible to assign a score for finding each of the 20 amino acid types at that position in some related protein structure. These scores are defined as "3D-1D scores". The 3D-1D scores can then be used in a sequence alignment algorithm to find the best alignment of a target amino acid sequence to a particular environment string. The quality of alignment is taken as a measure of the compatibility of the target sequence with the three-dimensional structure upon which the environment string was based.
  • the inventive method simulates the malleability of protein structures because no rigid tests for compatibility are applied.
  • gaps are allowed in the alignment of a target sequence to an environment string, and unfavorable amino acids can be placed at any position, provided these low scores are overcome by enough favorable amino acid-environment pairings (high 3D-1D scores).
  • high 3D-1D scores because the quality of the alignment to an environment string is not related to sequence similarity in any simple way, the sequence database searches using the environment strings are termed "3D compatibility searches" to distinguish them from homology searches.
  • the first step in the inventive method is to determine an environmental string for a protein having a known 3D structure. This is done as follows in the preferred embodiment:
  • each residue in the known three-dimensional protein structure is characterized in terms of A, f, and s values;
  • each position corresponding to a residue is assigned to one of 18 environment classes, based upon the A, f, and s values for the residue.
  • Six of the environment classes represent side-chain environments determined by A, the total area buried in the protein structure and f, the fraction of the side-chain area covered by polar atoms.
  • the environment of a side-chain is first classed as buried, partially buried, or exposed, according to the solvent-accessible surface area of the side-chain.
  • the buried and partially buried residue environments are further subdivided based on the fraction of the environment consisting of polar atoms.
  • the buried class is divided into three subclasses, labeled B, B + , and B ++ , in order of increasing environmental polarity.
  • the residue positions in the partially buried class are divided into two subclasses, labeled P and P + , in order of increasing polarity.
  • Water is treated as polar, so exposed positions are necessarily in a polar environment. Consequently, the exposed side-chain category, labeled X, is not subdivided into polarity classes.
  • the residue is placed in environment class B if f ⁇ 0.45; in environment class B + if 0.45 ⁇ f ⁇ 0.58; and in environment class B ++ if f ⁇ 0.58.
  • the residue is placed in environment category P if f ⁇ 0.67 and in environment class P + if f ⁇ 0.67.
  • a residue is placed in the exposed environment category X if less than 40 ⁇ 2 of the side-chain is buried.
  • residues in the side-chain environment classes are further distributed into three secondary structure types: ⁇ helix, ⁇ sheet and Other. This gives a total of 18 environment classes into which a particular residue may be categorized in the preferred embodiment (i.e., six side-chain environments times three secondary structure types).
  • the inventive method encompasses characterizing each residue of a known protein structure by n structural properties P 1 , P 2 , . . . P n at each position of the folded 3D protein structure.
  • the solvent-accessible surface area of each atom is determined by first centering a sphere at the nucleus of each protein atom (other than hydrogen).
  • the sphere has a radius equal to the sum of the van der Waals radius and the radius of a water molecule.
  • Each sphere was sampled at points placed about every 0.75 ⁇ along its surface. If a point was not within the sphere of any other atom, it is deemed accessible to water, otherwise it is treated as buried. The fraction of points accessible to water is then proportional to the solvent-accessible surface area.
  • the total area A of a side-chain buried in the protein structure is then determined by subtracting the free solvent accessibility of the side-chain (defined as the solvent-accessible area of side-chain X in a Gly-X-Gly tripeptide), and the total solvent-accessible area of the side-chain in the protein.
  • C ⁇ atoms were treated as part of the side-chain. Van der Waals radii were from T. J. Richmond, F. M. Richards, J. Mol Biol., 119, 537 (1978), and free side-chain areas were from D. A. Eisenberg, M. Wesson, M. Yamashita, Chemica Scripta, 29A, 217-221 (1989).
  • the fraction f of the side-chain covered by polar atoms is the number of sample points that are exposed to solvent or buried by polar atoms, divided by the total number of sample points. If a point is buried by both polar atoms and non-polar atoms, the closer type of atom takes precedence. Points covered by atoms of the side-chain under consideration were not counted in the determination of f.
  • the inventive method uses a variation of the profile method discussed above. While the profile method was originally developed for detecting sequence homology, it has been expanded to accommodate the purposes of the present invention. Using some of the concepts of the profile method, a 3D structure profile is generated for each environment string.
  • a 3D structure profile is a position-dependent scoring table in which each position of an environment string is assigned 20 scores ("3D-1D scores"), representing the likelihood of finding any of the 20 amino acids at that position.
  • these scores were based on sequence information from families of sequences. What distinguishes the present 3D structure profiles from sequence profiles is that now the profile scores are values based upon the structural environments of residues in a known three-dimensional structure, rather than simple sequence information.
  • a 3D structure profile table thus establishes a connection between a known three-dimensional protein structure, represented by a one-dimensional environmental string, and a one-dimensional target sequence, by specifying a 3D-1D score for each residue type in each environmental class.
  • the 3D-1D scores for matching the 20 common amino acids with the 18 defined environment classes used in the preferred embodiment are given in TABLE I.
  • the score for pairing a residue i with an environment j is given by the information value, ##EQU1## where P(ij) is the probability of finding residue i in environment j, and Pi is the overall probability of finding residue i in any environment.
  • these probabilities were determined statistically from a database of 16 known protein structures and sets of homologous sequences aligned to a sequence of known structure. The database used is described in R. Luthy, A. McLachlan, D. Eisenberg, Proteins: Structure, Function and Genetics, 10, 224-239 (1991) (hereby incorporated by reference).
  • the A, f, and s values for the residue were determined from the known 3D structure. Thereafter, the environment class for the residue position was determined, and the number of each residue type found at the position within the set of aligned sequences was counted. A residue type was counted only once per position. For example, if there were 10 aspartates and 1 glycine found at a position in a set of aligned sequences, then the aspartate and glycine counts were both incremented only by one. The total number of residue replacements in the database used was 8273. If the number of residues i in an environment j was found to be zero, the number was increased to 1 so that P(ij) was never zero.
  • Cutoffs for the environment categories shown in FIG. 2 were adjusted iteratively to maximize the total 3D-1D score summed over all residues in the database: ##EQU2## where N ij is the number of residues i in environment j. In this case, if N ij was zero, the number was not increased to 1. Instead, that term in the sum was treated as zero.
  • glutamine has unfavorable 3D-1D scores in the most non-polar, buried environment B, but is favorable in the polar, buried environment B ++ .
  • the preference for the secondary structure types generally follow the trends found in earlier studies. For example, lysine has a higher propensity to be in a helix than in a sheet. See P. Y. Chou, G. D. Fasman, Adv. Enz., 47, 45-148 (1978). A similar trend is seen in TABLE I.
  • the 3D-1D scores of TABLE I provide the link of 3D protein structure to 1D environment string sequence in the 3D structure profile method in the same way that the Dayhoff mutational matrix supplies the link between two sequences in the earlier sequence profile method.
  • FIG. 3 Part of the 3D structure profile table for sperm whale myoglobin is shown in FIG. 3.
  • Each row in the 3D structure profile represents an amino acid residue position in the known three-dimensional structure.
  • the second column gives the environment class of each residue position in the folded protein (i.e., the second column is the environment string for sperm whale myoglobin), determined as described above.
  • the following 20 columns for each residue position give the 3D-1D score for placing each of the 20 common amino acids in the environment found at that position in the structure.
  • the last two columns of FIG. 3 give penalty values for opening a gap (Opn) and for extending the length of the gap at a position (Ext).
  • Opn is set to be approximately three times the largest 3D-1D score in a row
  • Ext is set to be 1% of Opn.
  • the default value for Opn is 5, and for Ext is 0.05.
  • the values for Opn and Ext may be set based upon any desired criteria. For example, since gaps are known to occur most frequently between regions of secondary structure, it can be advantageous to lower the penalty values in non-secondary structure regions. In FIG. 3, the first two positions are not in a secondary structure, and are given low penalty values.
  • a user may specify weights, or multipliers, m to apply to the Opn and/or Ext values. Lower m values result in lesser penalties for opening or extending a gap.
  • the values for these multipliers m are 100 for every row of the profile, but are user adjustable.
  • the penalty multipliers may be different for the Opn and the Ext values.
  • the penalty value and penalty multipliers may be generated in other ways; one way is discussed in M. Gribskov, R. Luthy, and D. Eisenberg, Meth. in Enz. 183, 146 (1990).
  • the example shows the first 10 positions of the sperm whale myoglobin 3D structure profile.
  • the actual profile is 153 positions long, the length of the sperm whale myoglobin sequence.
  • the scores placed in each row are from the corresponding 3D-1D scores of TABLE I, multiplied by 100.
  • the most effective gap penalties were determined empirically. In this case, the gaps in helical regions were forbidden by setting very high gap penalties for the helical positions (positions 3 through 10 in the profile). In contrast, relatively low gap opening (Opn) and gap extension (Ext) penalties were used for the coil regions (positions 1 and 2).
  • each 3D-1D score is a frequency value derived from known protein sequences having known three-dimensional structures, each value being generated as the frequency of occurrence of the n structural properties P 1 , P 2 , . . . P n for each amino acid residue of the known protein sequences.
  • a 3D-1D score S(a) can be determined for each amino acid residue type a in a three-dimensional structure in terms of the values for n structural properties P 1 , P 2 , . . . P n in accordance with the following equation:
  • c 1 (a), c 2 (a), . . . c n (a) are empirically determined constants.
  • the constants c 1 (a), c 2 (a), . . . c n (a) are preferrably determined by a least squares fitting procedure applied to 20 equations S(a) i , one for each type a of the common amino acids.
  • the calculated profile score S(a) is fitted to the "observed" score of a sequence (not 3D structure) profile by varying the constants c 1 (a), c 2 (a), . . . c n (a).
  • other numerical or analytical fitting procedure may be used to determine c 1 (a), c 2 (a), . . . c n (a).
  • a comparison may be made between the table and amino acid sequences having unknown structures.
  • the inventive method determines the most favorable alignment of a target protein sequence S to the residue positions defined by the environment string E, and determines a "best fit" score S ij .
  • Each target sequence may then be further characterized by a ZScore, which is the number of standard deviations that S ij for the target sequence is above the mean alignment score for other target sequences of similar length.
  • the quality of alignment is taken as a measure of the compatibility of the target sequence with the three-dimensional structure upon which the environment string was based.
  • the dynamic programming algorithm defines the score S ij recursively as: ##EQU3## where S ij is the score for the alignment of the target sequence with the 3D structure profile such that position i of the target sequence is aligned with row j of the profile, and the penalties w k and w i are given by: ##EQU4## with m open and m extend being global penalty multipliers for the 3D structure profile, p open and p extend being the position-specific gap-opening (Opn) and gap-extension penalties (Ext), and j-k being the gap length.
  • the user can accept default values for m open and m extend , or enter other values when generating the profile.
  • the score S ij for the best alignment of the profile to each target sequence is tabulated, and the mean value and standard deviation of best alignment scores for all target sequences are computed.
  • the match of a target sequence to a 3D structure profile representing a particular protein fold is expressed quantitatively by its ZScore.
  • the ZScore for each target sequence is its number of standard deviations above the mean alignment score for other target sequences of similar length.
  • FIG. 4 shows the ZScores for all sequences in the database aligned to a 3D structure profile constructed from the coordinates of sperm whale myoglobin.
  • Myoglobin sequences are represented by black bars, other globin sequences are represented by white bars, and all other sequences are shown by gray bars.
  • results shown in FIG. 4 from the 3D structure profile are qualitatively similar to the results of a prior art sequence profile (not shown) constructed from the myoglobin sequence, but differs in two significant aspects.
  • sperm whale myoglobin is not the highest scoring protein sequence in the database. In a sequence homology search, the sperm whale myoglobin sequence must be the highest scoring sequence, since it will produce a perfect match.
  • the 3D structure profile was found to be somewhat more selective for globin sequences than is the sequence profile computed from the sperm whale myoglobin sequence. In general, it was found that a 3D structure profile is less sensitive to specific sequence relationships and more sensitive to general structural similarity than a sequence profile used with a sequence homology search.
  • CRP cyclic AMP receptor protein
  • RBP ribose binding protein
  • GBP galactose binding protein
  • ABP arabinose binding protein
  • FIG. 5a summarizes a sequence homology search using a prior art sequence profile constructed from the E. coil RBP sequence.
  • the bar graph shows the number of sequences that give a particular ZScore. A gap opening penalty of 45 and a gap extension penalty of 0.05 were used.
  • RBP2 S. typhimuri
  • FIG. 5b shows the results of a 3D compatibility search using a 3D structure profile constructed from the E. coil ribose binding protein structure in accordance with the present invention.
  • the bar graph shows the number of sequences that give a particular ZScore. A gap opening penalty of 5.0 and a gap extension penalty of 0.2 were used.
  • RBP1 E. coil ribo
  • the top scoring proteins in the sequence homology search are RBP and GBP.
  • the next highest scoring protein is pur repressor, which is a member of the lac repressor family.
  • the Zscore using the sequence profile is in the range (below 7) where spurious relationships can occur.
  • the next four highest scoring protein sequences are all member of the 70K heat shock protein family, three of which have ZScores above 7.
  • the 3D compatibility search indicates a structural correspondence between actin and members of the 70K heat shock protein family, a result unobtainable by a sequence homology search.
  • a "library" comprising a plurality of 3D structure profiles may be generated so that a new sequence of unknown structure may be compared to each of the library members to identify the most compatible structure corresponding to the sequence.
  • a further use would be to create a library of 3D structure profiles for fragments of known protein structures.
  • a new sequence of unknown structure may be compared to each of the library members to identify the most compatible structure fragments corresponding to subportions of the sequence.
  • the 3D structure of the sequence may be inferred to be similar to the sum of the corresponding structure fragments.
  • the inventive method may be used to assign a protein sequence to a 3D structure when no previous example of the structure exists.
  • Another use of the inventive method that is of significance is verification of protein models.
  • a problem in the determination of protein structure by x-ray crystallography or NMR is being certain that the final protein model is correct.
  • the main method of verification of an x-ray derived protein model is to compare the calculated x-ray pattern to the observed x-ray pattern (the R-factor). Verification of NMR models is a currently developing field. For many protein models determined from energy calculations, homology, or "inspired" guesswork, there is essentially no effective means of verification.
  • the present invention provides an effective method of verifying a protein model by generating a 3D structure profile from the coordinates of the model.
  • the 3D structure profile is then compared to the protein sequence on which the model was based. Tests have shown that a correct model of a protein structure results in a 3D structure profile that compares closely to the protein sequence, generating a high ZScore.
  • an incorrect model of the protein structure results in a 3D structure profile that does not compare as well with the protein sequence; the 3D structure profile does not recognize its "own" sequence with a significant ZScore.
  • Still another use of the invention is as a screening technique for determining protein sequences that have a structure similar or homologous to the structure of a known sequence.
  • This screening can be done in at least two ways. First, if the 3D structure of a protein is known, the inventive method can be used to screen a library of known sequences to determine structural analogs to the protein, as described above. The analogs can then be tested, using known techniques, for a desired biological activity, such as inhibition or stimulation of a receptor. Examples of such inhibition or stimulation are those occurring between a growth factor or a cytokine and their cell-membrane cell-membrane receptors. Those of ordinary skill in the art will know of other protein-receptor relationships to which the inventive screening method can be applied.
  • the inventive method can be used to compare the sequence of that protein to a library of 3D profiles representing known structures, as described above. Once a compatible 3D structure is determined (which itself is a structural analog to the original protein sequence), that structure can then be used to screen a library of known sequences to determine other structural analogs to the original protein sequence. As described above, the analogs can then be tested for biological function, such as their ability to stimulate or inhibit the interaction between the original protein and a binding partner.
  • Yet another use of the invention is for building three-dimensional models for protein sequences that have a structure similar or homologous to the structure of a known sequence.
  • a 3D structure profile is prepared form the known 3D structure.
  • the profile is then used according to the inventive method to screen a library of known sequences to determine structural analogs to the original protein sequence, as described above. From the known 3D structure, the protein backbone of the analogs can be determined.
  • Prediction of protein structures from amino acid sequences requires a link between three-dimensional structures and one-dimensional sequences.
  • this link is provided by the reverse approach of converting a three-dimensional structure to a one-dimensional string of environmental classes.
  • the complexity of three-dimensional space is eliminated, but the 3D-1D relationship at the heart of the protein folding problem is preserved in the 3D structure profile.
  • That related sequences can be detected by 3D structure profiles, which contain no direct information about amino acid type, might seem surprising. This suggests that the environmental classes based on area, polarity, and secondary structure are important parameters of folding.
  • the present invention marries two distinct lines in the study of proteins.
  • One is the sequence comparison and database searching line, the other is that of conformational energy calculations and consideration of stereochemistry and packing.
  • stereochemistry and energetics enter implicitly into the assignment of the environmental classes through the buried area of each residue and the polarity of atoms in the environment.
  • 3D compatibility searches are able to detect structural relationships that may not be apparent by sequence similarity. Thus, 3D compatibility searches should provide a useful complement to sequence homology searches in attacking the inverse protein folding problem.
  • the preferred embodiment of the inventive method starts with a known three-dimensional protein structure P and determines three key features of each residue's environment within the structure: (1) the total area A of the residue's side-chain that is buried by other protein atoms, inaccessible to solvent; (2) the fraction f of the side-chain area that is covered by polar atoms (O, N) or water; and (3) the local secondary structure s.
  • each position corresponding to a residue is assigned to one of a plurality of environment classes, based upon the A, f, and s values for the residue, thereby generating a one-dimensional environment string E which represents the environment class of each residue in the folded protein structure.
  • a 3D structure profile table T is created containing score values that represent the frequency of finding each of the 20 common amino acids from known protein structures at each position of the environment string E.
  • the method determines the most favorable alignment of a target protein sequence S to the residue positions defined by the environment string E, and determines a best fit score S ij from the structure profile table T.
  • a ZScore may be determined for the target sequence relative to a group of tested target sequences.

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